docs: def _get_known_noncoding_het_snp(data_dict):
          '''If ref is coding, return None. If the data dict has a known snp, and
             samtools made a call, then return the string ref_name_change and the
             % of reads supporting the variant type. If noncoding, but no
             samtools call, then return None'''
          if data_dict['gene'] == '1':
              return None
  
          if data_dict['known_var'] == '1' and data_dict['ref_ctg_effect'] == 'SNP' \
            and data_dict['smtls_nts'] != '.' and ';' not in data_dict['smtls_nts']:
              nucleotides = data_dict['smtls_nts'].split(',')
              depths = data_dict['smtls_nts_depth'].split(',')
  
              if len(nucleotides) != len(depths):
                  raise Error('Mismatch in number of inferred nucleotides from ctg_nt, smtls_nts, smtls_nts_depth columns. Cannot continue\n' + str(data_dict))
  
              try:
                  var_nucleotide = data_dict['known_var_change'][-1]
                  depths = [int(x) for x in depths]
                  nuc_to_depth = dict(zip(nucleotides, depths))
                  total_depth = sum(depths)
                  var_depth = nuc_to_depth.get(var_nucleotide, 0)
                  percent_depth = round(100 * var_depth / total_depth, 1)
              except:
                  return None
  
              return data_dict['known_var_change'], percent_depth
          else:
              return None